Triple
T130055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruth Bader Ginsburg |
E2634
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ruth |
E2634
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Ruth | Statement: [Ruth Bader Ginsburg, givenName, Ruth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruth Context triple: [Ruth Bader Ginsburg, givenName, Ruth]
-
A.
Ruth
chosen
Ruth is the given name of Ruth Bader Ginsburg, the pioneering U.S. Supreme Court Justice and prominent advocate for gender equality and civil rights.
-
B.
Rita
Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
-
C.
Miryam
Miryam is the Hebrew form of the name of the Virgin Mary, the mother of Jesus in Christian tradition.
-
D.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
E.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a2520c0f3481908b0ed054a2fca8d0 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257845c548190bfb49409988d1c57 |
completed | Feb. 28, 2026, 2:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2b85e7e4881909322c67388d64247 |
completed | Feb. 28, 2026, 9:41 a.m. |
Created at: Feb. 28, 2026, 2:30 a.m.